AI-based systems, such as Google's GenCast, have recently redefined the state of the art in weather forecasting, offering more accurate and timely predictions of both everyday weather and extreme events. While these systems are on the verge of replacing traditional meteorological methods, they also introduce new vulnerabilities into the forecasting process. In this paper, we investigate this threat and present a novel attack on autoregressive diffusion models, such as those used in GenCast, capable of manipulating weather forecasts and fabricating extreme events, including hurricanes, heat waves, and intense rainfall. The attack introduces subtle perturbations into weather observations that are statistically indistinguishable from natural noise and change less than 0.1% of the measurements—comparable to tampering with data from a single meteorological satellite. As modern forecasting integrates data from nearly one hundred satellites and many other sources operated by different countries, our findings highlight a critical security risk with the potential to cause large-scale disruptions and undermine public trust in weather forecasting.

A common paradigm for improving fuzzing performance is to focus on selected regions of a program rather than its entirety. While previous work has largely explored how these locations can be reached, their selection, that is, the where, has received little attention so far. In this paper, we fill this gap and present the first comprehensive analysis of target selection methods for fuzzing. To this end, we examine papers from leading security and software engineering conferences, identifying prevalent methods for choosing targets. By modeling these methods as general scoring functions, we are able to compare and measure their efficacy on a corpus of more than 1,600 crashes from the OSS-Fuzz project. Our analysis provides new insights for target selection in practice: First, we find that simple software metrics significantly outperform other methods, including common heuristics used in directed fuzzing, such as recently modified code or locations with sanitizer instrumentation. Next to this, we identify language models as a promising choice for target selection. In summary, our work offers a new perspective on directed fuzzing, emphasizing the role of target selection as an orthogonal dimension to improve performance.

Machine learning has been increasingly adopted for automatic security vulnerability discovery in research and industry. The ability to automatically identify and prioritize bugs in patches is crucial to organizations seeking to defend against potential threats. Previous works, however only consider bug discovery on statement, function or file level. How one would apply them to patches in realistic scenarios remains unclear. This paper presents a novel deep learning-based approach leveraging an interprocedural patch graph representation and graph neural networks to analyze software patches for identifying and locating potential security vulnerabilities. We modify current state-of-the-art learning-based static analyzers to be applicable to patches and show that our patch-based vulnerability discovery method, a context and flow-sensitive learning-based model, has a more than increased detection performance, is twice as robust against concept drift after model deployment and is particularly better suited for analyzing large patches. In comparison, other methods already lose their efficiency when a patch touches more than five methods.